Lab: Introduction to R
Basic Commands
x <- c(1, 3, 2, 5)
x
## [1] 1 3 2 5
x = c(1, 6, 2)
x
## [1] 1 6 2
y = c(1, 4, 3)
length(x)
## [1] 3
length(y)
## [1] 3
x + y
## [1] 2 10 5
ls()
## [1] "A" "Auto" "cylinders" "f" "fa" "x"
## [7] "y"
rm(x, y)
ls()
## [1] "A" "Auto" "cylinders" "f" "fa"
rm(list = ls())
?matrix
x <- matrix(data = c(1, 2, 3, 4), nrow = 2, ncol = 2)
x
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
x <- matrix(c(1, 2, 3, 4), 2, 2)
matrix(c(1, 2, 3, 4), 2, 2, byrow = TRUE)
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
sqrt(x)
## [,1] [,2]
## [1,] 1.000000 1.732051
## [2,] 1.414214 2.000000
x^2
## [,1] [,2]
## [1,] 1 9
## [2,] 4 16
x <- rnorm(50)
y <- x + rnorm(50, mean = 50, sd = .1)
cor(x, y)
## [1] 0.995529
set.seed(1303)
rnorm(50)
## [1] -1.1439763145 1.3421293656 2.1853904757 0.5363925179 0.0631929665
## [6] 0.5022344825 -0.0004167247 0.5658198405 -0.5725226890 -1.1102250073
## [11] -0.0486871234 -0.6956562176 0.8289174803 0.2066528551 -0.2356745091
## [16] -0.5563104914 -0.3647543571 0.8623550343 -0.6307715354 0.3136021252
## [21] -0.9314953177 0.8238676185 0.5233707021 0.7069214120 0.4202043256
## [26] -0.2690521547 -1.5103172999 -0.6902124766 -0.1434719524 -1.0135274099
## [31] 1.5732737361 0.0127465055 0.8726470499 0.4220661905 -0.0188157917
## [36] 2.6157489689 -0.6931401748 -0.2663217810 -0.7206364412 1.3677342065
## [41] 0.2640073322 0.6321868074 -1.3306509858 0.0268888182 1.0406363208
## [46] 1.3120237985 -0.0300020767 -0.2500257125 0.0234144857 1.6598706557
set.seed(3)
y <- rnorm(100)
mean(y)
## [1] 0.01103557
var(y)
## [1] 0.7328675
sqrt(var(y))
## [1] 0.8560768
sd(y)
## [1] 0.8560768
Graphics
x <- rnorm(100)
y <- rnorm(100)
plot(x, y)
plot(x, y, xlab = "this is the x-axis",
ylab = "this is the y-axis",
main = "Plot of X vs Y")
pdf("Figure.pdf")
plot(x, y, col = "green")
dev.off()
## RStudioGD
## 2
x <- seq(1, 10)
x
## [1] 1 2 3 4 5 6 7 8 9 10
x <- 1:10
x
## [1] 1 2 3 4 5 6 7 8 9 10
x <- seq(-pi, pi, length = 50)
y <- x
f <- outer(x, y, function(x, y) cos(y) / (1 + x^2))
contour(x, y, f)
contour(x, y, f, nlevels = 45, add = T)
fa <- (f - t(f)) / 2
contour(x, y, fa, nlevels = 15)
image(x, y, fa)
persp(x, y, fa)
persp(x, y, fa, theta = 30)
persp(x, y, fa, theta = 30, phi = 20)
persp(x, y, fa, theta = 30, phi = 70)
persp(x, y, fa, theta = 30, phi = 40)
Indexing Data
A <- matrix(1:16, 4, 4)
A
## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16
A[2, 3]
## [1] 10
A[c(1, 3), c(2, 4)]
## [,1] [,2]
## [1,] 5 13
## [2,] 7 15
A[1:3, 2:4]
## [,1] [,2] [,3]
## [1,] 5 9 13
## [2,] 6 10 14
## [3,] 7 11 15
A[1:2, ]
## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
A[, 1:2]
## [,1] [,2]
## [1,] 1 5
## [2,] 2 6
## [3,] 3 7
## [4,] 4 8
A[1, ]
## [1] 1 5 9 13
A[-c(1, 3), ]
## [,1] [,2] [,3] [,4]
## [1,] 2 6 10 14
## [2,] 4 8 12 16
A[-c(1, 3), -c(1, 3, 4)]
## [1] 6 8
dim(A)
## [1] 4 4
Loading Data
Auto <- read.table("Auto.data")
View(Auto)
head(Auto)
## V1 V2 V3 V4 V5 V6 V7 V8
## 1 mpg cylinders displacement horsepower weight acceleration year origin
## 2 18.0 8 307.0 130.0 3504. 12.0 70 1
## 3 15.0 8 350.0 165.0 3693. 11.5 70 1
## 4 18.0 8 318.0 150.0 3436. 11.0 70 1
## 5 16.0 8 304.0 150.0 3433. 12.0 70 1
## 6 17.0 8 302.0 140.0 3449. 10.5 70 1
## V9
## 1 name
## 2 chevrolet chevelle malibu
## 3 buick skylark 320
## 4 plymouth satellite
## 5 amc rebel sst
## 6 ford torino
Auto <- read.table("Auto.data", header = T, na.strings = "?", stringsAsFactors = T)
View(Auto)
Auto <- read.csv("Auto.csv", na.strings = "?", stringsAsFactors = T)
View(Auto)
dim(Auto)
## [1] 397 9
Auto[1:4, ]
## mpg cylinders displacement horsepower weight acceleration year origin
## 1 18 8 307 130 3504 12.0 70 1
## 2 15 8 350 165 3693 11.5 70 1
## 3 18 8 318 150 3436 11.0 70 1
## 4 16 8 304 150 3433 12.0 70 1
## name
## 1 chevrolet chevelle malibu
## 2 buick skylark 320
## 3 plymouth satellite
## 4 amc rebel sst
Auto <- na.omit(Auto)
dim(Auto)
## [1] 392 9
names(Auto)
## [1] "mpg" "cylinders" "displacement" "horsepower" "weight"
## [6] "acceleration" "year" "origin" "name"
Additional Graphical and Numerical Summaries
plot(Auto$cylinders, Auto$mpg)
attach(Auto)
## The following objects are masked from Auto (pos = 3):
##
## acceleration, cylinders, displacement, horsepower, mpg, name,
## origin, weight, year
## The following objects are masked from Auto (pos = 4):
##
## acceleration, cylinders, displacement, horsepower, mpg, name,
## origin, weight, year
plot(cylinders, mpg)
cylinders <- as.factor(cylinders)
plot(cylinders, mpg)
plot(cylinders, mpg, col = "red")
plot(cylinders, mpg, col = "red", varwidth = T)
plot(cylinders, mpg, col = "red", varwidth = T,
horizontal = T)
plot(cylinders, mpg, col = "red", varwidth = T,
xlab = "cylinders", ylab = "MPG")
hist(mpg)
hist(mpg, col = 2)
hist(mpg, col = 2, breaks = 15)
pairs(Auto)
pairs(
~ mpg + displacement + horsepower + weight + acceleration,
data = Auto
)
plot(horsepower, mpg)
identify(horsepower, mpg, name)
## integer(0)
summary(Auto)
## mpg cylinders displacement horsepower weight
## Min. : 9.00 Min. :3.000 Min. : 68.0 Min. : 46.0 Min. :1613
## 1st Qu.:17.00 1st Qu.:4.000 1st Qu.:105.0 1st Qu.: 75.0 1st Qu.:2225
## Median :22.75 Median :4.000 Median :151.0 Median : 93.5 Median :2804
## Mean :23.45 Mean :5.472 Mean :194.4 Mean :104.5 Mean :2978
## 3rd Qu.:29.00 3rd Qu.:8.000 3rd Qu.:275.8 3rd Qu.:126.0 3rd Qu.:3615
## Max. :46.60 Max. :8.000 Max. :455.0 Max. :230.0 Max. :5140
##
## acceleration year origin name
## Min. : 8.00 Min. :70.00 Min. :1.000 amc matador : 5
## 1st Qu.:13.78 1st Qu.:73.00 1st Qu.:1.000 ford pinto : 5
## Median :15.50 Median :76.00 Median :1.000 toyota corolla : 5
## Mean :15.54 Mean :75.98 Mean :1.577 amc gremlin : 4
## 3rd Qu.:17.02 3rd Qu.:79.00 3rd Qu.:2.000 amc hornet : 4
## Max. :24.80 Max. :82.00 Max. :3.000 chevrolet chevette: 4
## (Other) :365
summary(mpg)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 17.00 22.75 23.45 29.00 46.60